Alternative splicing is now well established as a widespread phenomenon in higher eukaryotes, and amajor contributor to proteome diversity. Over half of the multiexonic human genes are believed to havesplice variants Large-scale detection of alternative splicing usually involves expressed sequence tags(ESTs) or microarray analysis. However, due to various sampling biases, not all alternative splicingevents can be detected by these methods. Moreover, nowadays genomic sequence data is being churnedout at a much faster rate than transcript data, that is, several genomes do not have a very high amountof transcript data. This situation is likely to continue for the foreseeable future. Thus, there is a need forindependent methods of detecting alternative splicing. Previous studies have shown that discriminativefeatures can be used to distinguish alternatively splice exons from constitutively spliced ones. We usedBayesian Networks, a state of the art machine learning tool, to accurately distinguish conservedalternative exons from conserved constitutive ones. Using a combination of previously describedfeatures and novel ones, we were able to achieve a classification performance competitive with thestate of the art from the literature (Dror et al, Bioinformatics. 2005 Apr 1;21(7):897-90). Future plansinclude prediction without using conservation based features, prediction of species-specific alternativesplicing, and prediction of alternative splicing in human-specific exons.